Finding the distance between latlong - python-3.x

Question

I am a bit stuck. I have a CSV which includes:
Site Name
Latitude
Longitude.
This CSV has 100,000 locations. I need to generate a comma separated list for each location, showing the other locations within 5KM
I have tried the attached, which transposes the table & gives me 100,000 columns with 100,000 rows and the distance populated as the result. But I am not sure how to just make a new pandas column which has a list of all the sites within 5KM.
Can you help?
from geopy.distance import geodesic
def distance(row, csr):
lat = row['latitude']
long = row['longitude']
lat_long = (lat, long)
try:
return round(geodesic(lat_long, lat_long_compare).kilometers,2)
except:
return 9999
for key, value in d.items():
lat_compare = value['latitude']
long_compare = value['longitude']
lat_long_compare = (lat_compare, long_compare)
csr = key
df[key] = df.apply([distance, csr], axis=1)
Some sample data can be:
destinations = { 'bigben' : {'latitude': 51.510357,
'longitude': -0.116773},
'heathrow' : {'latitude': 51.470020,
'longitude': -0.454295},
'alton_towers' : {'latitude': 52.987662716,
'longitude': -1.888829778}
}
bigben is 0.8KM from the London Eye
heathrow is 23.55KM from the London Eye
alton_towers is 204.63KM from the London Eye
So, in this case, the field should show only big ben.
So we get:
Site | Sites within 5KM
28, BigBen

Answer 1

Here is one way with NearestNeighbors.
from sklearn.neighbors import NearestNeighbors
# data from your input
df = pd.DataFrame.from_dict(destinations, orient='index').rename_axis('Site Name').reset_index()
radius = 50 #change to whatever, in km
# crate the algo with the raidus and the metric for geospatial distance
neigh = NearestNeighbors(radius=radius/6371, metric='haversine')
# fit the data in radians
neigh.fit(df[['latitude', 'longitude']].to_numpy()*np.pi/180)
# extract result and transform to get the expected output
df[f'Site_within_{radius}km'] = (
pd.Series(neigh.radius_neighbors()[1]) # get a list of index for each row
.explode()
.map(df['Site Name']) # get the site name from row index
.groupby(level=0) # transform back to row-row relation
.agg(list) # can use ', '.join instead of list
)
print(df)
Site Name latitude longitude Site_within_50km
0 bigben 51.510357 -0.116773 [heathrow]
1 heathrow 51.470020 -0.454295 [bigben]
2 alton_towers 52.987663 -1.888830 [nan]

Answer 2

Another way
from sklearn.neighbors import DistanceMetric
from math import radians
import pandas as pd
import numpy as np
#To Radians
df['latitude'] = np.radians(df['latitude'])
df['longitude'] = np.radians(df['longitude'])
#Pair the cities
df[['latitude','longitude']].to_numpy()
#Assume a sperical radius of 6373
dist = DistanceMetric.get_metric('haversine')#DistanceMetric class df=pd.DataFrame(dist.pairwise(df[['latitude','longitude']].to_numpy())*6373,columns=df.index.unique(), index=df.index.unique())
s=df.gt(0)&df.le(50)
df['Site_within_50km']=s.agg(lambda x: x.index[x].values, axis=1)#Filter
bigben heathrow alton_towers Site_within_50km
bigben 0.000000 23.802459 203.857533 [heathrow]
heathrow 23.802459 0.000000 195.048961 [bigben]
alton_towers 203.857533 195.048961 0.000000 []